There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Additive manufacturing of metallic components – Process, structure and properties

The interfacial tension between the molten metal and the surrounding plasma environment affects the circulation of molten metal in the weld pool, heat transfer, and the eventual structure and properties of the weld metal. Since the effect of plasma on the interfacial tension of molten metals is not known, interfacial tension values between low pressure argon plasma and pure copper and iron were measured. The variables studied were temperature and the intensity of plasma emission.

Interfacial tension between low pressure argon plasma and molten copper and iron

The evolution of the grain structure and topological-class distributions in zone-refined iron were modeled using a three-dimensional (3-D) Monte Carlo (MC) model. The effect of grain size on topological features was examined. The relationship between the topological features of grains and the geometry of their surrounding grains was studied. In particular, the average number of sides of the grains was related to the average number of sides of their neighbors. Both the computed grain-size distribution and the topological-class distribution were found to be invariant with time. A linear relationship existed between the average grain size and the average number of sides of grains. The number of sides of grains was inversely proportional to the average number of sides of the neighboring grains. The computed average grain size, grain-size distribution, and topological-class distribution agreed well with the corresponding independent experimental data. The results indicate significant promise for understanding grain growth and topological features using 3-D MC simulation.

Three-dimensional Monte Carlo simulation of grain growth in zone-refined iron

Although nitrogen concentrations at levels much higher than Sieverts' Law predictions during the arc welding of iron and steel are well established, there is currently no commonly accepted methodology to determine this concentration quantitatively. The nature and concentrations of various species in the plasma phase above the weld pool surface are therefore investigated in the present work using both theoretical and experimental techniques. A comprehensive thermodynamic analysis of the nitrogen containing plasma phase of a gas tungsten welding arc shows that ionised species dominate close to the electrode, whereas neutral monatomic and diatomic nitrogen are the primary species near the metal surface at plasma temperatures as low as 5000 K. When oxygen is added to a nitrogen containing plasma, the resulting nitrogen concentration in the weld metal is further enhanced. Definitive proof is provided for a mechanism in which nitrogen and oxygen species interact in the plasma phase at temperatures below...

Enhanced dissolution of nitrogen during gas tungsten arc welding of steels

Asymmetry in steel welds with dissimilar amounts of sulfur

Although numerical calculations of heat transfer and fluid flow can provide detailed insights into welding processes and welded materials, these calculations are complex and unsuitable in situations where rapid calculations are needed. A recourse is to train and validate a neural network, using results from a well tested heat and fluid flow model to significantly expedite calculations and ensure that the computed results conform to the basic laws of conservation of mass, momentum and energy. Seven feedforward neural networks were developed for gas metal arc (GMA) fillet welding, one each for predicting penetration, leg length, throat, weld pool length, cooling time between 800uC and 500uC, maximum velocity and peak temperature in the weld pool. Each model considered 22 inputs that included all the welding variables, such as current, voltage, welding speed, wire radius, wire feed rate, arc efficiency, arc radius, power distribution, and material properties such as thermal conductivity, specific heat and temperature coefficient of surface tension. The weights in the neural network models were calculated using the conjugate gradient (CG) method and by a hybrid optimisation scheme involving the CG method and a genetic algorithm (GA). The neural network produced by the hybrid optimisation model produced better results than the networks based on the CG method with various sets of randomised initial weights. The CG method alone was unable to find the best optimal weights for achieving low errors. The hybrid optimisation scheme helped in finding optimal weights through a global search, as evidenced by good agreement between all the outputs from the neural networks and the corresponding results from the heat and fluid flow model.

Tarasankar Debroy

Papers

Interfacial tension between low pressure argon plasma and molten copper and iron

Three-dimensional Monte Carlo simulation of grain growth in zone-refined iron

Enhanced dissolution of nitrogen during gas tungsten arc welding of steels

Asymmetry in steel welds with dissimilar amounts of sulfur

Neural network model of heat and fluid flow in gas metal arc fillet welding based on genetic algorithm and conjugate gradient optimisation